Machine and equipment assets are engineered to perform particular tasks as part of a business process. For example, assets can include, among other things and without limitation, industrial manufacturing equipment on a production line, drilling equipment for use in mining operations, wind turbines that generate electricity on a wind farm, transportation vehicles, and the like. As another example, assets may include devices that aid in diagnosing patients such as imaging devices (e.g., X-ray or MRI systems), monitoring equipment, and the like. The design and implementation of these assets often takes into account both the physics of the task at hand, as well as the environment in which such assets are configured to operate.
Low-level software and hardware-based controllers have long been used to drive machine and equipment assets. However, the rise of inexpensive cloud computing, increasing sensor capabilities, and decreasing sensor costs, as well as the proliferation of mobile technologies, have created opportunities for creating novel industrial and healthcare based assets with improved sensing technology and which are capable of transmitting data that can then be distributed throughout a network. As a consequence, there are new opportunities to enhance the business value of some assets through the use of novel industrial-focused hardware and software.
In an industrial operational environment, a digital representation of an asset is made up of a variety of operational technology (OT) and information technology (IT) data management systems. Examples of OT data systems include data historian services which may maintain a history of sensor data streams from sensors attached to an asset and condition monitors that detect and store alerts and alarms related to potential fault conditions of an asset. Examples of IT data systems include enterprise resource planning (ERP) systems and maintenance record databases. Each of these systems operates in a narrow information silo with its own semantics, concerns and data storage models.
An industrial asset management application may attempt to aggregate these various sources of data and information into a centralized location in order to integrate them and apply analytics, visualization techniques, and other processes that help human operators detect issues and make decisions with respect to an asset. From a network perspective, the IT and OT data systems can be thought of as comprising the network “Edge” whereas the asset management application can be thought of as existing in the “Cloud.”
However, in emerging digital industrial technologies, industrial enterprises are beginning to move from an asset management orientation to a business outcome orientation to better achieve desired results. For example, airlines are moving from a focus on purchasing and managing individual aircraft to optimizing key performance indicators (KPIs) such as cost and passenger/miles per unit of time over an operational environment which includes a fleet of aircraft, airports, employees, and the like. The application and platform requirements for providing effective decision support for such optimized business outcomes goes beyond traditional data integration, analytic and visualization approaches embodied in existing asset management solutions.